CN105627504A - Energy consumption estimation method for water chilling unit of variable-air-volume central air conditioning system based on support vector machine - Google Patents

Abstract

The invention discloses an energy consumption estimation method for a water chilling unit of a variable-air-volume central air conditioning system based on a support vector machine. The energy consumption estimation method comprises the following steps: screening easy-to-acquire and independent running parameters obviously affecting the energy consumption of the water chilling unit; and adopting a support vector machine regression model to establish a water chilling unit energy consumption estimation model, and using the model to estimate the energy consumption of the water chilling unit. The energy consumption estimation method not only has the characteristic that parameters are common and easy to measure, but also has the advantage that the support vector machine solves a nonlinear regression problem. Tests show that the model is effective, and thus, a novel method is provided for diagnosing the failure of the central air conditioning system, estimating the service life of the central air conditioning system and ensuring that the user is clear about expenditure.

Description

Variable air volume central air-conditioner system handpiece Water Chilling Units energy consumption method of estimation based on support vector machine

Technical field

The present invention relates to central air conditioner system energy consumption and estimate field, specifically a kind of variable air volume central air-conditioner system handpiece Water Chilling Units energy consumption method of estimation based on support vector machine.

Background technology

China's building energy consumption accounts for whole society's energy consumption proportion to be increased year by year, from 2005 27.5% to 2014 years 33%, and central air conditioner system energy consumption accounts for building energy consumption 40-60%, handpiece Water Chilling Units is as central air-conditioning nucleus equipment, and its energy consumption accounts for more than the 40% of central air conditioner system energy consumption; Meanwhile, design for power supply and distribution present situation makes user generally understand central air conditioner system whole power consumption or handpiece Water Chilling Units device packets power consumption. Therefore, research central air-conditioning handpiece Water Chilling Units energy consumption method of estimation can ensure user's transparent consumption; Meanwhile, if the energy consumption based on handpiece Water Chilling Units operational factor can be set up estimate that model is beneficial to fault detect and the life prediction of handpiece Water Chilling Units and central air conditioner system.

From domestic and international present Research, central air-conditioning handpiece Water Chilling Units energy consumption method of estimation is broadly divided into regression analysis and the intelligent algorithm such as neutral net, regression tree. But, at present about the operational factor selecting appreciable impact handpiece Water Chilling Units energy consumption, only consider the dependency between operational factor and handpiece Water Chilling Units energy consumption, not yet consider the dependency between operational factor; Meanwhile, existing research method relates to architectural modulus more, but obtains some architectural modulus (as heat transfer coefficient of window, wall compare to ground area) and great effort need to be spent; And support vector machine has the features such as better generalization ability and globally optimal solution, provide a kind of effective method for the modeling of handpiece Water Chilling Units energy consumption.

Accordingly, it would be desirable to a kind of parameter easily obtains and the central air-conditioning handpiece Water Chilling Units energy consumption method of estimation independent, modeling method is scientific and reasonable.

Summary of the invention

In view of this, it is an object of the invention to overcome defect of the prior art, it is provided that can a kind of parameter easily obtain and the variable air volume central air-conditioner system handpiece Water Chilling Units energy consumption method of estimation based on support vector machine independent, modeling method is scientific and reasonable.

The variable air volume central air-conditioner system handpiece Water Chilling Units energy consumption method of estimation based on support vector machine of the present invention, comprises the following steps: adopting correlation analysis to filter out from each central air-conditioning operational factor affects handpiece Water Chilling Units energy consumption and input parameter significantly and ensure to be absent from strong correlation between this input parameter;

The method adopting support vector machine to be modeling sets up energy consumption model;

Energy consumption model is utilized to estimate handpiece Water Chilling Units energy consumption;

Further, calculate the Pearson correlation coefficients between each described central air-conditioning operational factor and handpiece Water Chilling Units power, the absolute value of the Pearson correlation coefficients between the handpiece Water Chilling Units power central air-conditioning operational factor more than or equal to 0.45 is extracted as the operational factor stronger with handpiece Water Chilling Units energy consumption dependency;

Further, described screening affect handpiece Water Chilling Units energy consumption and inputs the step of parameter significantly and include: the Pearson correlation coefficients between the operational factor that calculating is stronger with handpiece Water Chilling Units energy consumption dependency between two; If the absolute value of the Pearson correlation coefficients calculated is more than 0.7, extracts the wherein operational factor stronger with handpiece Water Chilling Units energy consumption dependency common, that be prone to detection and input parameter significantly as affecting handpiece Water Chilling Units energy consumption;

Further, described energy consumption model is:

$f\left(x\right)=\mathrm{sgn}(\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{a}_i^{*}{y}_i\exp (-\mathrm{\γ}{\left|\right|x-x_i\left|\right|}^2)+b)$

Wherein, | | x-x_i| | being two norm distances, x is input set, x_iFor supporting vector, �� is-g parameter, and �� > 0, b is biasing, and n represents and supports vector number,Represent that i-th supports the coefficient of vector.

Further, utilize described energy consumption model to estimate before handpiece Water Chilling Units energy consumption, the described handpiece Water Chilling Units energy consumption that affects is inputted parameter significantly carries out after data normalization the input data as described energy consumption model; After utilizing described energy consumption model to obtain output data, described output data are carried out renormalization and obtains actual handpiece Water Chilling Units energy consumption data.

The invention has the beneficial effects as follows: the variable air volume central air-conditioner system handpiece Water Chilling Units energy consumption method of estimation based on support vector machine of the present invention, screening appreciable impact handpiece Water Chilling Units energy consumption and be easily obtained, independent operational factor; Adopt Support vector regression model to set up handpiece Water Chilling Units energy consumption estimate model and use this model to estimate handpiece Water Chilling Units energy consumption, the existing parameter of this invention is common and the feature of easy measurement, having again support vector machine to solve the advantage of nonlinear regression problem, after tested, this model is effective. This is central air conditioner system fault diagnosis and life prediction, ensures that user's transparent consumption provides new method.

Accompanying drawing explanation

Below in conjunction with drawings and Examples, the invention will be further described:

Fig. 1 is the variable air volume central air-conditioner system handpiece Water Chilling Units energy consumption method of estimation flow chart based on support vector machine of the present invention;

The handpiece Water Chilling Units energy consumption that affects that Fig. 2 is the present invention inputs choice of parameters flow chart significantly.

Detailed description of the invention

Fig. 1 is the variable air volume central air-conditioner system handpiece Water Chilling Units energy consumption method of estimation flow chart based on support vector machine of the present invention; The handpiece Water Chilling Units energy consumption that affects that Fig. 2 is the present invention inputs choice of parameters flow chart significantly, as shown in the figure, the variable air volume central air-conditioner system handpiece Water Chilling Units energy consumption method of estimation based on support vector machine of the present embodiment, comprises the following steps: adopting correlation analysis to filter out from each central air-conditioning operational factor affects handpiece Water Chilling Units energy consumption and input parameter significantly and ensure to be absent from strong correlation between this input parameter; The method adopting support vector machine to be modeling sets up energy consumption model; Energy consumption model is utilized to estimate handpiece Water Chilling Units energy consumption; Adopt Support vector regression model to set up handpiece Water Chilling Units energy consumption estimate model and use this model to estimate handpiece Water Chilling Units energy consumption, the existing parameter of this invention is common and the feature of easy measurement, having again support vector machine to solve the advantage of nonlinear regression problem, after tested, this model is effective. This is central air conditioner system fault diagnosis and life prediction, ensures that user's transparent consumption provides new method.

In the present embodiment, calculate the Pearson correlation coefficients between each described central air-conditioning operational factor and handpiece Water Chilling Units power, the central air-conditioning operational factor corresponding more than or equal to 0.45 with the absolute value of the Pearson correlation coefficients between described handpiece Water Chilling Units power is extracted as the operational factor stronger with handpiece Water Chilling Units energy consumption dependency; Pearson correlation coefficients r formula is:

$r=\frac{\underset{i=1}{\overset{n}{\mathrm{\Σ}}}(X_i-\stackrel{\‾}{X})(Y_i-\stackrel{\‾}{Y})}{\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{(X_i-X)}^2\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{(Y_i-\stackrel{\‾}{Y})}^2}$

Wherein, X_iAnd Y_iRespectively two parameter sample value,The numeric distribution of r is between [-1,1], and corresponding degree of correlation is as shown in table 1:

Table 1 correlation coefficient r meaning

r	0.00	0.00...��0.3	��0.30...��0.50	��0.50...��0.80	��0.80...��1.00
						Degree of correlation	Without relevant	Micro-positive negative correlation	Real positive negative correlation	Notable positive negative correlation	Highly positive negative correlation

In the present embodiment, described screening affect handpiece Water Chilling Units energy consumption and inputs the step of parameter significantly and include: the Pearson correlation coefficients between the operational factor that calculating is stronger with handpiece Water Chilling Units energy consumption dependency between two; If the absolute value of the Pearson correlation coefficients calculated is more than 0.7, extracts the wherein operational factor stronger with handpiece Water Chilling Units energy consumption dependency common, that be prone to detection and input parameter significantly as affecting handpiece Water Chilling Units energy consumption;

In the present embodiment, the method selecting support vector machine to be modeling, support vector machine has advantage in solution nonlinear regression problem, can still obtain globally optimal solution under condition of small sample.

Support vector machine Selection of kernel function Radial basis kernel function, formula is as follows:

K(x_i, x)=exp (-�� | | x-x_i||²)

Wherein, | | x-x_i| | being two norm distances, x is input set, x_iFor supporting vector, �� is-g parameter, �� > 0. In model training, adopting cross-validation method to compare Mean Square Error (MeanSquaredError, MSE) and determine major parameter (penalty parameter c and kernel functional parameter g) value, MSE formula is as follows:

$\mathrm{MSE}=\frac{1}{n}\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{(f\left(x_i\right)-y_i)}^2$

Training obtains shown in handpiece Water Chilling Units energy consumption model such as formula (5):

$f\left(x\right)=\mathrm{sgn}(\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{a}_i^{*}{y}_i\exp (-\mathrm{\γ}{\left|\right|x-x_i\left|\right|}^2)+b)$

Wherein, b is biasing, and n represents and supports vector number,Represent that i-th supports the coefficient of vector.

In the present embodiment, utilize described energy consumption model to estimate before handpiece Water Chilling Units energy consumption, the described handpiece Water Chilling Units energy consumption that affects is inputted parameter significantly carries out after data normalization the input data as described energy consumption model; After utilizing described energy consumption model to obtain output data, described output data being carried out renormalization and obtains actual handpiece Water Chilling Units energy consumption data, normalize between [-1,1] respectively by input, output data, formula is:

$z^{\′}=2\·\left(\frac{z-z_{\min }}{z_{\max }-z_{\min }}\right)-1$

Wherein, z is initial data, and z' is data after normalization; Utilize energy consumption model to estimate handpiece Water Chilling Units energy consumption, obtain initial results, initial results is reduced to actual size by renormalization, renormalization formula:

$y^{\′}=\frac{(f\left(x_i\right)+1)\·(y_{\max }-y_{\min })}{2}+y_{\min }$

Wherein, y' is the estimation output valve after renormalization, f (x_i) estimating output valve for model, y is original output. According to said method, in air conditioning system with variable, handpiece Water Chilling Units is as object of study, central air-conditioning handpiece Water Chilling Units energy consumption operational factor comes from the intelligent building management system (IBMS) of certain office building, handpiece Water Chilling Units active power data from electric energy management system (EMS) data platform, the record time in May, 2012��JIUYUE. By choice of parameters, extract appreciable impact handpiece Water Chilling Units energy consumption parameter: chilled water supply backwater temperature difference; Select the training of the data after normalization in 13 days Mays in 2012 to obtain handpiece Water Chilling Units energy consumption and estimate model, test 6��JIUYUE in 2012 handpiece Water Chilling Units energy consumption of 119 days, statistical test result such as following table after renormalization,

Test statistics result

Note: daily power consumption relative error 5% represents that handpiece Water Chilling Units estimates that the relative error natural law in 5% between daily power consumption and actual measurement daily power consumption accounts for the ratio of this month total effective natural law.

What finally illustrate is, above example is only in order to illustrate technical scheme and unrestricted, although the present invention being described in detail with reference to preferred embodiment, it will be understood by those within the art that, technical scheme can be modified or equivalent replacement, without deviating from objective and the scope of technical solution of the present invention, it all should be encompassed in the middle of scope of the presently claimed invention.

Claims (5)

1. the variable air volume central air-conditioner system handpiece Water Chilling Units energy consumption method of estimation based on support vector machine, it is characterised in that comprise the following steps:

Adopt correlation analysis to filter out from each central air-conditioning operational factor to affect handpiece Water Chilling Units energy consumption and input parameter significantly and ensure to be absent from strong correlation between this input parameter;

The method adopting support vector machine to be modeling sets up energy consumption model;

Energy consumption model is utilized to estimate handpiece Water Chilling Units energy consumption.

2. the variable air volume central air-conditioner system handpiece Water Chilling Units energy consumption method of estimation based on support vector machine according to claim 1, it is characterized in that: calculate the Pearson correlation coefficients between each described central air-conditioning operational factor and handpiece Water Chilling Units power, and the Pearson correlation coefficients between handpiece Water Chilling Units power is not less than the central air-conditioning operational factor of 0.45 is extracted as the operational factor stronger with handpiece Water Chilling Units energy consumption dependency.

3. the variable air volume central air-conditioner system handpiece Water Chilling Units energy consumption method of estimation based on support vector machine according to claim 2, it is characterised in that: described screening affects handpiece Water Chilling Units energy consumption and inputs the step of parameter significantly and include:

Calculate the Pearson correlation coefficients between described between two and the operational factor that handpiece Water Chilling Units energy consumption dependency is stronger;

If the absolute value of the Pearson correlation coefficients calculated is more than 0.7, extracts the wherein operational factor stronger with handpiece Water Chilling Units energy consumption dependency common, that be prone to detection and input parameter significantly as affecting handpiece Water Chilling Units energy consumption.

4. the variable air volume central air-conditioner system handpiece Water Chilling Units energy consumption method of estimation based on support vector machine according to claim 3, it is characterised in that described energy consumption model is:

$f\left(x\right)=\mathrm{sgn}(\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{a}_i^{*}{y}_i\exp (-\mathrm{\γ}{\left|\right|x-x_i\left|\right|}^2)+b)$

Wherein, | | x-x_i| | being two norm distances, x is input set, x_iFor supporting vector, �� is-g parameter, and �� > 0, b is biasing, and n represents and supports vector number,Represent that i-th supports the coefficient of vector.

5. the variable air volume central air-conditioner system handpiece Water Chilling Units energy consumption method of estimation based on support vector machine according to claim 4, it is characterized in that: utilize described energy consumption model to estimate before handpiece Water Chilling Units energy consumption, the described handpiece Water Chilling Units energy consumption that affects is inputted parameter significantly carries out after data normalization the input data as described energy consumption model; After utilizing described energy consumption model to obtain output data, described output data are carried out renormalization and obtains actual handpiece Water Chilling Units energy consumption data.